模拟水环境下的压紧弹簧刚度试验及计算模型的研究

在模拟水环境下，对堆内构件压紧弹簧的缩比试样进行了刚度试验，并与有限元模拟、基于小扰度理论模型和大扰度随动模型的分析结果进行比较分析。结果表明，当摩擦系数按文献实验测值0.189取值时，有限元模拟、基于小扰度的理论模型和大扰度随动模型计算所得的刚度值均与试验所得的相近；卸载稳定段的刚度均明显小于加载稳定段的，是加载时的0.6倍左右。有限元模拟分析进一步阐明，在压紧弹簧变形过程中，压紧弹簧的截面存在转动，它与垫板之间的接触点并不是固定的，在加载和卸载过程中存在来回的径向位移。并且压紧弹簧接触面上的摩擦力方向是相反的，使得压紧弹簧在加载和卸载过程的刚度存在较大的差别。较小扰度理论模型、考虑压紧弹簧截面转动和接触点径向位移的大扰度随动模型所得的结果与有限元模拟更为接近。      

一种大型复杂构件加工新模式及新装备探讨

大型复杂构件是航空航天、能源、船舶等领域装备的核心结构件，此类构件通常具有尺寸大、形状复杂、刚性弱等特点。传统“分体离线加工-在线检测”模式存在工艺不稳定、过程复杂、柔性差、周期长等问题，以龙门式多轴数控机床加工为代表的“包容式”加工模式，难以适应大型复杂构件的高效高质量加工制造需求。提出一种基于移动式和吸附式机器人的多机协同原位加工新模式，通过多机器人系统自主寻位、精确定位加工与加工质量原位检测，实现大型复杂构件多安装面并行铣削、制孔与打磨等作业。多机器人系统包括移动式混联机器人、吸附式并联机器人、移动式串联铣削机器人、移动式双臂加工机器人和移动式打磨机器人。构建多机协同原位加工模式，需要揭示多机器人协同原位加工行为与大型弱刚性结构件质量控制的交互机理，面临着本体、测量、工艺和集成四个方面的挑战，需要设计高灵活、高刚度的移动式和吸附式加工机器人，解决移动机器人自主准确寻位和超大结构件原位高精检测难题，攻克加工变形误差在线补偿和振动抑制技术，通过集成实现多机协同高效高精加工，为大型复杂构件的高效高质量制造提供创新技术及装备，并实现此类构件制造核心技术及装备自主可控。     

Interplay of the phase and the chemical composition of the powder feedstock on the properties of porous 8YSZ thermal barrier coatings

Some chemical impurities enhance sintering kinetics of ceramic Thermal Barrier Coatings (TBCs) which can cause their premature failure during operation in gas turbine engine by causing reduction in coating’s strain compliance as well as faster bond-coat oxidation due to increased thermal conductivity. Certain chemical impurities are also believed to suppress resistance to tetragonal to monoclinic phase transformation in 8YSZ, which can also be an important factor regarding TBC’s performance. Most of the impurities and some of the monoclinic phase present in the powder feedstocks can survive into the as-sprayed coating. Therefore, there is a general trend towards OEMs requiring the lowest amounts of chemical impurities and the lowest amounts of monoclinic phase in the powder feedstocks. This paper presents a comprehensive investigation aimed at understanding the role and the relative importance of the chemical and phase purities of the powder feedstock for the properties and performance of thick 8YSZ TBCs.     

Discriminative graph convolution networks for hyperspectral image classification

Recently, the proposal of graph convolutional networks (GCN) has successfully implemented into hyperspectral image data representation and analysis. In spite of the great success, there are still several major challenges in hyperspectral image classification, including within-class diversity, and between-class similarity, which generally degenerate hyperspectral image classification performance. To address the problems, we propose a discriminative graph convolution networks (DGCN) for hyperspectral image classification. This method introduces the concepts of within-class scatter and between-class scatter, which respectively reflect the global geometric structure and discriminative information of the input space. The experimental results on the hyperspectral data sets show that the proposed method has good classification performance.     

Characterization of the structure and properties of processed alumina-graphene and alumina-zirconia composites

The onset of hybrid alumina-based composites, which combines two or more nano-particles within the alumina matrix has already shown promising improvements in the matrix material. However, variations in mechanical properties including the optimum compositions that give improved properties faced with the development of alumina-based composites require further studies to understand the underlying mechanisms and synergistic effects of the nano-particle additions on the alumina matrix. In the current study, the structure and properties of Al?O?-graphene (0.5 wt%) and Al?O?–ZrO? (4 wt% and 10 wt%) composites fabricated via hot-pressing was studied as a baseline for multiple combinations. Even though the addition of 10 wt%ZrO? resulted in a 23% reduction in the grain size of the alumina matrix, the 4 wt%ZrO? addition resulted in a 14% increase in grain size as compared to the parent alumina matrix. X-ray diffraction analysis revealed that there was approximately 85% monoclinic (m-ZrO₂) vs. 15% tetragonal (t-ZrO₂) crystal structures in the A4ZrO? sample whilst the A₁₀ZrO? had approximately 93% m-ZrO₂ vs. 7% t-ZrO₂. The high-volume fraction of the monoclinic crystal structures in the A₁₀ZrO? accounts for the induced microcracks in the sample since the transition from the ductile-tetragonal to brittle-monoclinic is associated with the exertion of compressive stresses on the alumina matrix by the associated elastic volume expansion of m-ZrO₂. Also, the addition of 0.5 wt%graphene resulted in about 37% reduction in the grain size of the alumina matrix, and approximately 10% increase in hardness as a result of the distribution of graphene along the grain boundaries of the parent alumina matrix, which restricts grain coalescence and growth during processing. Furthermore, an increase up to 115% and 164% were observed in the fracture toughness (K_IC) with the inclusion of 0.5 wt%graphene and 10 wt%ZrO? respectively, which was primarily ascribed to the fine-grained microstructures and toughening mechanisms of the intergranular graphene and ZrO? particles.     

煤/石油焦共气化过程中AAEM的催化转化行为

以富含碱金属及碱土金属的准东煤和石油焦为原料,在热重反应器上分别进行了水蒸气及CO2条件下的共气化实验,探究了AAEM的赋存形态对其转化行为及燃料气化特性的影响规律.研究表明,准东煤与石油焦在水蒸气条件下的共气化反应速率明显快于CO2条件,来自煤中的AAEM促进了石油焦的气化.在CO2气氛中,不同赋存形态矿物的催化作用存在较大差异,盐酸溶态的Ca起主要催化作用,并在气化过程中生成CaS.而在水蒸气气氛中,不同赋存形态矿物的催化作用差异较小,气化过程中含Ca组分主要生成CaO.     

基于多参数特征保留的载荷谱加速耐久性编辑

为了得到时间更短加载效果相同的加速耐久性试验载荷谱,提出了基于多参数特征保留的载荷谱编辑方法。该方法同时考虑载荷谱的损伤、功率谱密度以及统计参数等信息,对零部件载荷谱的时间进行压缩。以汽车悬架螺旋弹簧的载荷谱为例,采用该方法进行缩减,同时从多个参数特征方面与传统的基于损伤保留的编辑方法所得到的载荷谱进行对比。为了进一步验证编辑效果,采用编辑谱和原始谱对弹簧进行疲劳仿真。结果表明,该方法能够有效缩短汽车零部件的载荷谱,可得到与原始载荷谱具有相同加载效果的编辑载荷谱。     

电力电子技术在新能源领域中应用探究

为了促进新能源的充分利用,解决能源需求量大问题,借助电力电子技术,对非电力能源有效转化,促进不同资源的电力转化能力,在本文中笔者就电力电子技术在新能源领域中的应用进行探究.